I think that, in approaching AI, one should try to find a theory that accounts for all cognitive phenomena observed in humans (and potentially for other cognitive phenomena not observed in humans, that one wants to see in one's AI). However, I think that oftentimes a relatively compact *theory* can explain a lot of surface-level phenomena. This is where Minsky and I differ. He wants the theory-of-mind to explicitly account for all observed human cognitive phenomena, whereas I think many of them should be accounted for largely *implicitly*, as a consequence of other deeper constructs in the theory of mind.
Finally, I note that the explicit/implicit dichotomy isn't really a dichotomy. In many cases, in the brain and in Novamente, it's the case that some phenomenon is "localized" in some particular component, yet the real essence of the phenomenon lies in *the overall system dynamics that the particular component triggers*. For instance, an emotion may be triggered via a particular localized component, yet the essence of the emotion may lie in the overall system dynamics to which the activity in that component gives rise. In this case one has a mix of implicit and explicit foundations for the cognitive phenomenon in this question. In this language, my complaint is that when Minsky brews this mix, he adds too much explicitness and not enough implicitness... He has localized agents dealing with particular emotions. In Novamente we may have particular nodes triggering particular emotions, but emotions themselves are system-wide dynamics attractors triggered by the activation of these particular nodes. Minsky's design, as articulated, lacks any attention to this kind of system-wide self-organizing dynamics. The complexity that should go into the attractors that emerge when a localized component associated with a function is triggered, is put into the component itself. -- Ben G > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > Behalf Of J.Andrew Rogers > Sent: Sunday, October 24, 2004 2:32 PM > To: [EMAIL PROTECTED] > Subject: [agi] Model simplification and the kitchen sink > > > On Oct 24, 2004, at 7:05 AM, Ben Goertzel wrote: > > One idea proposed by Minsky at that conference is something I disagree > > with > > pretty radically. He says that until we understand human-level > > intelligence, we should make our theories of mind as complex as > > possible, > > rather than simplifying them -- for fear of leaving something out! > > This > > reminds me of some of the mistakes we made at Webmind Inc. Contra > > Minsky > > and Webmind, in Novamente I've sought to create the simplest possible > > design > > that accounts for all the diverse phenomena of mind on an emergent > > level. > > Minsky is really trying to jam every aspect of the mind into his > > design on > > the explicit level. > > > It is hard for me to tell if you are actually disagreeing with Minsky > here. I agree that one should throw absolutely everything into the mix > when building a solution, but probably for different reasons than > Minsky has. > > > Chemical engineering has an interesting problem space that is fairly > unique in its structure, and consequently developed general problem > solving algorithms that I still find to be very unique and at the same > time enormously useful in theoretical computer science. For whatever > reason, these algorithms and techniques just don't seem to all that > familiar to most people in practice, which may be part of why chemical > engineering is perceived as being such a difficult discipline to learn. > A major component of what chemical engineering is about is taking > arbitrarily complex systems (a mixture of chemical, thermal, and > mechanical), with the assumption of inconsistent and incomplete > information, and then solving, reducing, transforming, and modeling > said system to a quasi-deterministic system that you can run more > conventional calculations on with some degree of predictive accuracy. > This is very frequently an obscenely complex optimization problem > because no tidy mathematical solution exists, often but not always a > consequence of the facts about the nature of the system being > inconsistent, incomplete, or not quite correct. > > As a chemical engineering student, you are taught to take absolutely > every fact, equation, and expected result you can think of or discover > before starting with the reduction to quasi-determinism. As a > heuristic, the more information and weird edge cases you put into the > system, the better the predictive quality of the final product. Now, a > lot of people get the impression that this leads to information > overload, but there are established algorithms and techniques to > systematically reduce all this complexity and one finds with experience > that by throwing *more* stuff into the pile, it is to actually easier > to build a good model and makes resolving inconsistencies and > uncertainties to reasonable or adequate values easier. More > information tends to produce superior models via induction. > > From this perspective, one cannot build an accurate "simple" model > unless one starts with a very complex model that includes everything > remotely related to the system in question no matter how irrelevant > seeming. As often happens in chemical engineering systems, the > exclusion of equations and facts to make a simpler initial model very > frequently reduces the quality and accuracy of the simplified model. > Furthermore, there is no theoretical justification for exclusion > because that is predicated on having a good model of the system in the > first place. Yet virtually every approach to AGI design (and much > software design in general) is based on starting with a simple model > and building upward to a "correct" model, or starting with complexity > and not even bothering to properly reduce it, very questionable methods > for solving non-toy system models that contain uncertainties or are > incomplete. A problem with this is that you usually end up at a > different "correct" solution working from the simple model upward than > if you start with a complex model and work downward, and the downward > model will almost always be more accurate if developed by someone > accustomed to working that way. Unfortunately, system reduction and > modeling algorithms and heuristics of the kind used in chemical > engineering do not seem to be taught in computer science even though > they have always been eminently relevant as far as I could tell. > > My perspective of AGI and theoretical computer science in general has > long been influenced by my history with chemical engineering and I have > found it immensely useful for tackling very tough design space problems > in computer science (and not just AGI), especially since computer > science tends to be more consistent than chemical engineering as a > general rule. It is something of a truism that half of chemical > engineers end up working in computer science, probably because many > design problems in computer science look like really clean versions of > chemical engineering system problems. My model of AGI now is clean and > elegant, but I started by throwing everything and the kitchen sink into > the original pile and systematically reducing that to a deterministic > system model a la chemical engineering. If I forgot something > important, I just had to re-model the system and account for the diffs > in the solution, a fairly mechanical exercise that didn't really > require me throwing things away. > > So yes, I think starting with the most complex model possible is a > superior method, given that one has system reduction skills. AGI is a > dirty enough theoretical system space currently that I don't see how a > good solution can be arrived at any other way; it would not really be > possible with any other kind of system and I don't see why AGI would be > an exception. I'm sure people will disagree though. Computer science > and chemical engineering both deal with the same kinds of systems, yet > they developed very different methodologies for modeling them, > primarily because computer science assumes a kind of logical perfection > in the assumptions of a system and chemical engineering assumes dirty > and imperfect information. > > cheers, > > j. andrew rogers > > ------- > To unsubscribe, change your address, or temporarily deactivate > your subscription, > please go to http://v2.listbox.com/member/[EMAIL PROTECTED] > > ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
